The percentile_width argument will be removed in a future
version. Use percentiles instead.
width of the desired uncertainty interval, default is 50,
which corresponds to lower=25, upper=75

percentiles : array-like, optional

The percentiles to include in the output. Should all
be in the interval [0, 1]. By default percentiles is
[.25, .5, .75], returning the 25th, 50th, and 75th percentiles.

include, exclude : list-like, ‘all’, or None (default)

Specify the form of the returned result. Either:

None to both (default). The result will include only numeric-typed
columns or, if none are, only categorical columns.

A list of dtypes or strings to be included/excluded.
To select all numeric types use numpy numpy.number. To select
categorical objects use type object. See also the select_dtypes
documentation. eg. df.describe(include=[‘O’])

If include is the string ‘all’, the output column-set will
match the input one.

For object dtypes (e.g. timestamps or strings), the index
will include the count, unique, most common, and frequency of the
most common. Timestamps also include the first and last items.

For mixed dtypes, the index will be the union of the corresponding
output types. Non-applicable entries will be filled with NaN.
Note that mixed-dtype outputs can only be returned from mixed-dtype
inputs and appropriate use of the include/exclude arguments.

If multiple values have the highest count, then the
count and most common pair will be arbitrarily chosen from
among those with the highest count.